微机械振动陀螺仪的误差分析

介绍了微机械振动陀螺仪的结构特点及工作机理,建立了其力学模型,并在此基础上,对原理误差进行了定性的分析,得出了影响其精度的主要干扰源,为误差的补偿指明了方向.     

《测量学》实验教学改革之探索

从测量实验时间安排、实验教学方法、多媒体教学手段和实验报告评定等方面论述了《测量学》实验教学的改革。通过这四个方面的改革,培养了学生独立完成测量实验的能力,并且在扩大招生的情况下,保证了《测量学》实验教学任务的完成与质量。     

Using ranked set sampling with binary outcomes in cluster randomized designs

We study the use of ranked set sampling (RSS) with binary outcomes in cluster-randomized designs (CRDs), where a generalized linear mixed model (GLMM) is used to model the hierarchical data structure involved. Under the GLMM-based framework, we propose three different approaches to estimate the treatment effect, including the nonparametric (NP), maximum likelihood (ML) and pseudo likelihood (PL) estimators. We investigate their asymptotic properties and examine their finite-sample performance via simulation. Based on these three RSS estimators, we further develop procedures for testing the existence of the treatment effect. We examine the power and size of our proposed RSS tests and compare them with existing tests based on simple random sampling (SRS). All the proposed RSS estimation and test methods are illustrated with two data examples, one for rare events and the other for non-extreme events. Throughout our investigations, we also consider the possible effect of imperfect ranking. Among the proposed methods, we provide recommendations on whether to use RSS rather than SRS with binary outcomes in CRDs and, if yes, when to use which RSS method. The Canadian Journal of Statistics 48: 342–365; 2020 © 2019 Statistical Society of Canada     

Partial order relations for classification comparisons

The Bayes classification rule offers the optimal classifier, minimizing the classification error rate, whereas the Neyman–Pearson lemma offers the optimal family of classifiers to maximize the detection rate for any given false alarm rate. These motivate studies on comparing classifiers based on similarities between the classifiers and the optimal. In this article, we define partial order relations on classifiers and families of classifiers, based on rankings of rate function values and rankings of test function values, respectively. Each partial order relation provides a sufficient condition, which yields better classification error rates or better performance on the receiver operating characteristic analysis. Various examples and applications of the partial order theorems are discussed to provide comparisons of classifiers and families of classifiers, including the comparison of cross-validation methods, training data that contains outliers, and labelling errors in training data. The Canadian Journal of Statistics 48: 152–166; 2020 © 2019 Statistical Society of Canada     

Some finite-sample-size properties of rosenblatt density estimates

The results of a Monte Carlo study of the sensitivity of Rosenblatt density estimates to the scale factor are presented.     

An explicit formula for the risk of James-Stein estimators

Expressions for the risk of a Stein estimator and its principal derivatives involve a well-defined. but nonevaluated. expectation term. Stein (1966) has suggested a second-order approximation. In this paper we present an alternative exact expression.     

Measures of mortality in the study of individuals infected with the human immunodeficiency virus in the UK haemophilia population

Two statistical issues that have arisen in the course of a study of mortality and disease related to the human immunodeficiency virus (HIV) in the haemophilia population of the UK are discussed. The first of these concerns methods of standardization for age and it is shown that, when the mortality of HIV-infected individuals with different severities of haemophilia are compared, an analysis based on the ratio of observed to national expected deaths suggests that mortality in HIV-infected individuals depends on the severity of their haemophilia. This conclusion is inappropriate and mortality in HIV-infected individuals is, in fact, similar regardless of severity of haemophilia. The second part of the paper discusses the effect of using various end points for studies of survival and progression of HIV-related disease. In the present example it was possible to calculate relative survival in HIV-infected individuals, i.e. survival after correcting for mortality expected in the absence of HIV infection. An analysis based on absolute survival gave a very similar picture of the effect of age at infection to an analysis based on relative survival, whereas an analysis based on the time to diagnosis of acquired immune deficiency syndrome (AIDS) underestimated the effect substantially and the possible alternative end point of time to AIDS or HIV-related death was shown to be subject to considerable misclassification error.     

Partially functional linear varying coefficient model

In this paper, we introduce a new partially functional linear varying coefficient model, where the response is a scalar and some of the covariates are functional. By means of functional principal components analysis and local linear smoothing techniques, we obtain the estimators of coefficient functions of both function-valued variable and real-valued variables. Then the rates of convergence of the proposed estimators and the mean squared prediction error are established under some regularity conditions. Moreover, we develop a hypothesis test for the model and employ the bootstrap procedure to evaluate the null distribution of test statistic and the p-value of the test. At last, we illustrate the finite sample performance of our methods with some simulation studies and a real data application.     

Consistent Smooth Bootstrap Kernel Intensity Estimation for Inhomogeneous Spatial Poisson Point Processes

Non‐parametric estimation and bootstrap techniques play an important role in many areas of Statistics. In the point process context, kernel intensity estimation has been limited to exploratory analysis because of its inconsistency, and some consistent alternatives have been proposed. Furthermore, most authors have considered kernel intensity estimators with scalar bandwidths, which can be very restrictive. This work focuses on a consistent kernel intensity estimator with unconstrained bandwidth matrix. We propose a smooth bootstrap for inhomogeneous spatial point processes. The consistency of the bootstrap mean integrated squared error (MISE) as an estimator of the MISE of the consistent kernel intensity estimator proves the validity of the resampling procedure. Finally, we propose a plug‐in bandwidth selection procedure based on the bootstrap MISE and compare its performance with several methods currently used through both as a simulation study and an application to the spatial pattern of wildfires registered in Galicia (Spain) during 2006.